Model-based Sensor Fault Diagnosis Research Articles

Functional Safety Analysis and Design of Sensors in Robot Joint Drive System

The reliable operation of the sensors of robot joint drive systems (RJDs) is a key factor in ensuring the safety of equipment and personnel. Over the years, additional safety-related systems have been designed to prevent safety incidents caused by robot failures, ignoring the functional safety issues of the robot sensors themselves. In view of this, based on IEC61508, a functional safety analysis and design method for sensors of RJDs is proposed in this paper. Firstly, the hazard analysis and risk assessment clarified the goals that the safety protection function of the RJD’s sensor should achieve. Then, by establishing the motor drive model and transmission model, a model-based sensor fault diagnosis and isolation strategy is proposed. Considering the fault-tolerant operation of system, a fail-operational hardware architecture of the safety-related system is designed. Markov analysis shows that the safety integrity level (SIL) of safety-related systems can reach SIL3. Finally, experiments are designed to validate the proposed fault diagnosis and fault tolerance strategy. The results show that the safety-related system can effectively locate sensor failures, realize fault-tolerant control when a single sensor fails and perform safe torque off (STO) protection when multiple sensors fail.

Distributed model-based sensor fault diagnosis of marine fuel engines

This paper proposes a distributed model-based methodology for the detection and isolation of sensor faults in marine fuel engines. The proposed method considers a Mean Value First Principle model and a wide selection of heterogeneous sensors for monitoring the engine components. The detection of faults is realised based on residuals generated using nonlinear Differential Algebraic estimators combined with adaptive thresholds. The isolation of faults is, then, realised in two levels; local sensor fault detection and isolation agents are designed to monitor specific sensor sets and aim to detect faults in these sets; and a global decision logic is designed to isolate multiple sensor faults that may be propagated between the local monitoring agents. Finally, simulation results are used to illustrate the application of this method and its efficiency.

A Model-Based Sensor Fault Diagnosis Scheme for Batteries in Electric Vehicles

The implementation of each function of a battery management system (BMS) depends on sensor data. Efficient sensor fault diagnosis is essential to the durability and safety of battery systems. In this paper, a model-based sensor fault diagnosis scheme and fault-tolerant control strategy for a voltage sensor and a current sensor are proposed with recursive least-square (RLS) and unscented Kalman filter (UKF) algorithms. The fault diagnosis scheme uses an open-circuit voltage residual generator and a capacity residual generator to generate multiple residuals. In view of the different applicable state of charge (SOC) intervals of each residual, different residuals need to be selected according to the different SOC intervals to evaluate whether a sensor fault occurs during residual evaluation. The fault values of the voltage and current sensors are derived in detail based on the open-circuit voltage residual and the capacity residual, respectively, and applied to the fault-tolerant control of battery parameters and state estimations. The performance of the proposed approaches is demonstrated and evaluated by simulations with MATLAB and experimental studies with a commercial lithium-ion battery cell.

Aeroengine Control System Sensor Fault Diagnosis Based on CWT and CNN

The aeroengine control system is a piece of complex thermal machinery which works under high-speed, high-load, and high-temperature environmental conditions over lengthy periods of time; it must be designed for the utmost reliability and safety to function effectively. The consequences of sensor faults are often extremely serious. The inherent complexity of the engine structure creates difficulty in establishing accurate mathematical models for the model-based sensor fault diagnosis. This paper proposes an intelligent fault diagnosis method for aeroengine sensors combining a deep learning algorithm with time-frequency analysis wherein the signal recognition problem is transformed into an image recognition problem. The continuous wavelet transform (CWT) is first applied to seven common health condition signals in an engine control system sensor in order to generate scalograms that capture the characteristics of the signal. A convolutional neural network (CNN) model trained with preprocessed and labeled datasets is then used to extract the features of a time-frequency graph based on which faults can be identified and isolated. This method does not require modeling and design thresholds, so it has strong robustness and accuracy rate of over 97%. The trained model effectively reveals faults in sensor signals and allows for accurate identification of fault types.

A Sensor Fault Diagnosis Method for a Lithium-Ion Battery Pack in Electric Vehicles

In electric vehicles, a battery management system highly relies on the measured current, voltage, and temperature to accurately estimate state of charge (SOC) and state of health. Thus, the normal operation of current, voltage, and temperature sensors is of great importance to protect batteries from running outside their safe operating area. In this paper, a simple and effective model-based sensor fault diagnosis scheme is developed to detect and isolate the fault of a current or voltage sensor for a series-connected lithium-ion battery pack. The difference between the true SOC and estimated SOC of each cell in the pack is defined as a residual to determine the occurrence of the fault. The true SOC is calculated by the coulomb counting method and the estimated SOC is obtained by the recursive least squares and unscented Kalman filter joint estimation method. In addition, the difference between the capacity used in SOC estimation and the estimated capacity based on the ratio of the accumulated charge to the SOC difference at two nonadjacent sampling times can also be defined as a residual for fault diagnosis. The temperature sensor which is assumed to be fault-free is used to distinguish the fault of a current or voltage sensor from the fault of a battery cell. Then, the faulty current or voltage sensor can be isolated by comparing the residual and the predefined threshold of each cell in the pack. The experimental and simulation results validate the effectiveness of the proposed sensor fault diagnosis scheme.

Model-based Sensor Fault Diagnosis of Vehicle Suspensions with a Support Vector Machine

In this paper, a means of generating residuals based on a quarter-car model and evaluating them using a support vector machine (SVM) is proposed. The proposed model-based residual generator shows very robust performance regardless of unknown road surface conditions. In addition, an SVM classifier without empirically set thresholds is used to evaluate the residuals. The proposed method is expected to reduce the effort required to design fault diagnosis algorithms. While an unknown input observer is used to generate the residual, the relative velocity of the vehicle suspension is obtained additionally. The proposed algorithm is verified using commercial vehicle simulator Carsim with Matlab & Simulink. As a result, the fault diagnosis algorithm proposed in this paper can detect sensor faults that cannot be detected by a limit checking method and can reduce the effort required when designing algorithms.

LPV Model Based Sensor Fault Diagnosis and Isolation for Permanent Magnet Synchronous Generator in Wind Energy Conversion Systems

This paper deals with the current sensor fault diagnosis and isolation (FDI) problem for a permanent magnet synchronous generator (PMSG) based wind system. An observer based scheme is presented to detect and isolate both additive and multiplicative faults in current sensors, under varying torque and speed. This scheme includes a robust residual generator and a fault estimation based isolator. First, the PMSG system model is reformulated as a linear parameter varying (LPV) model by incorporating the electromechanical dynamics into the current dynamics. Then, polytopic decomposition is introduced for H ∞ design of an LPV residual generator and fault estimator in the form of linear matrix inequalities (LMIs). The proposed gain-scheduled FDI is capable of online monitoring three-phase currents and isolating multiple sensor faults by comparing the diagnosis variables with the predefined thresholds. Finally, a MATLAB/SIMULINK model of wind conversion system is established to illustrate FDI performance of the proposed method. The results show that multiple sensor faults are isolated simultaneously with varying input torque and mechanical power.

Model-based Sensor Fault Diagnosis of a Lithium-ion Battery in Electric Vehicles

The battery critical functions such as State-of-Charge (SoC) and State-of-Health (SoH) estimations, over-current, and over-/under-voltage protections mainly depend on current and voltage sensor measurements. Therefore, it is imperative to develop a reliable sensor fault diagnosis scheme to guarantee the battery performance, safety and life. This paper presents a systematic model-based fault diagnosis scheme for a battery cell to detect current or voltage sensor faults. The battery model is developed based on the equivalent circuit technique. For the diagnostic scheme implementation, the extended Kalman filter (EKF) is used to estimate the terminal voltage of battery cell, and the residual carrying fault information is then generated by comparing the measured and estimated voltage. Further, the residual is evaluated by a statistical inference method that determines the presence of a fault. To highlight the importance of battery sensor fault diagnosis, the effects of sensors faults on battery SoC estimation and possible influences are analyzed. Finally, the effectiveness of the proposed diagnostic scheme is experimentally validated, and the results show that the current or voltage sensor fault can be accurately detected.

Robust model-based sensor fault monitoring system for nonlinear systems in sensor networks.

A new model-based sensor fault diagnosis (FD) scheme, using an equivalent model, is developed for a kind of Multiple Inputs Multiple Outputs (MIMO) nonlinear system which fulfills the Lipschitz condition. The equivalent model, which is a bank of one-dimensional linear state equations with the bounded model uncertainty, can take the place of a plant's exact nonlinear model in the case of sensor FD. This scheme shows a new perspective whereby, by using the equivalent model, it doesn't have to study the nonlinear internal structure character or get the exact model. The influence of the model uncertainty on the residuals is explained in this paper. A method, called pretreatment, is utilized to minimize the model uncertainty. The eigenstructure assignment method with assistant state is employed to solve the problem of perfect decoupling against the model uncertainty, disturbance, system faults, the relevant actuator faults, or even the case of no input from the relevant actuator. The realization of the proposed scheme is given by an algorithm according to a single sensor FD, and verified by a simulation example. Depending on the above, a sensor fault monitoring system is established by the sensor network and diagnosis logic, then the effectiveness is testified by a simulation.

Combined Signal and Model-Based Sensor Fault Diagnosis for a Doubly Fed Induction Generator

The problem of multiplicative and/or additive fault detection and isolation (FDI) in the current sensors of a doubly fed induction generator (DFIG) is considered in the presence of model uncertainty. A residual generator based on the DFIG model is proposed using the structure of the classical generalized observer scheme. However, each observer in this scheme is replaced by a robust H_/H∞ fault detection filter followed by a Kalman-like observer. The latter further attenuates the effect of the modeling uncertainties on the residuals. It exploits the specific pattern induced by the balanced three-phase nature of all the electric signals. It turns out that the FDI problem then amounts to detecting an abrupt change in the mean of the residual vector in the additive fault case, or the appearance of sine waves superimposed on a white noise vector in the multiplicative fault case. A decision algorithm made of a combination of generalized likelihood ratio algorithms allows us to detect and isolate the additive and multiplicative sensor faults. The complete FDI system is tested through simulations on a controlled DFIG.

Fault Diagnostics for Turbo-Shaft Engine Sensors Based on a Simplified On-Board Model

Combining a simplified on-board turbo-shaft model with sensor fault diagnostic logic, a model-based sensor fault diagnosis method is proposed. The existing fault diagnosis method for turbo-shaft engine key sensors is mainly based on a double redundancies technique, and this can't be satisfied in some occasions as lack of judgment. The simplified on-board model provides the analytical third channel against which the dual channel measurements are compared, while the hardware redundancy will increase the structure complexity and weight. The simplified turbo-shaft model contains the gas generator model and the power turbine model with loads, this is built up via dynamic parameters method. Sensor fault detection, diagnosis (FDD) logic is designed, and two types of sensor failures, such as the step faults and the drift faults, are simulated. When the discrepancy among the triplex channels exceeds a tolerance level, the fault diagnosis logic determines the cause of the difference. Through this approach, the sensor fault diagnosis system achieves the objectives of anomaly detection, sensor fault diagnosis and redundancy recovery. Finally, experiments on this method are carried out on a turbo-shaft engine, and two types of faults under different channel combinations are presented. The experimental results show that the proposed method for sensor fault diagnostics is efficient.

Analysis of model-based sensor fault diagnosis with application to a motor-driven power steering system

This paper presents a theoretical analysis of model-based fault detection and isolation (FDI) and its application to a motor-driven power steering (MDPS) system. In particular, this paper proposes a systematic approach to determining fault detectability and isolability of a linear time-invariant system based on a parity equation. The proposed approach is applied to the MDPS system in order to examine the fault detectability and isolability of the MDPS depending on the number and type of sensors and disturbances. Fault detection and isolation of the phase current sensors and the angular velocity sensor are conducted using a parity equation derived from the mathematical model of the permanent magnet alternating current (PMAC) motor. The fault of the steering torque sensor is detected and isolated by cross-checking with two different torque estimates. An adaptive threshold is proposed to improve the performance and robustness of the proposed FDI method against time-varying model uncertainties. Simulation and vehicle test results show the validity and performance of the proposed FDI method for the MDPS system.

Robust Dead Reckoning System with Interacting Multiple Model Based Sensor Fault Diagnosis for Mobile Robot

An interacting multiple-model (IMM) approach to sensor fault detection and identification (FDI) in the dead reckoning of mobile robots is proposed. Changes of sensor normal/failure modes are explicitly modeled as switching from one mode to another in a probabilistic manner, and the sensor fault diagnosis and the robot pose estimation are achieved via a bank of parallel Kalman filters. The dead reckoning algorithm is implemented on a skid-steered type of robot, where the thirty-two system modes (one normal mode and thirty-one hard sensor-failure modes) of five sensors (four wheel-encoders and one yaw-rate gyro) are handled. Experimental results validate the effectiveness of the dead reckoning system.